gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

text stringlengths 0 820
is 2x2 with stride 2 that outputs a feature map at 2x the in-
put resolution (28 in Figure 2), followed by a LayerNorm
and GELU, and then another 2x2 deconvolution layer that
outputs a feature maps at 2x the previous resolution (56 in
Figure 2). See the supplementary material for a full architec-
tural diagram.
Reconstruction After having been upsampled, the lower
resolution and higher resolution feature maps are passed into
Laplacian Blocks (LBs in Figure 2) that reconstruct high
and low resolution images for the high and low frequency
reconstruction, respectively. Architecturally, the Laplacian
Blocks consist of a sequence of three sub-blocks: a Lapla-
cian Feature Mapping Block, a Laplacian Upsample Block,
and a Laplacian Pyramid Reconstruction Block. The Feature
Mapping Block is used to project features within a particular
layer of the Laplacian Pyramid back to the RGB space. The
Laplacian Upsample Block represents a learnable upsam-
ple function that maps latent features from one layer of the
Laplacian Pyramid to a higher level. Finally, the Laplacian
Pyramid Reconstruction Block is used to reconstruct infor-
mation at the different frequencies in RGB space. Following
super resolution literature [2], an L1 loss is used for high
frequency output to better reconstruct edges and an L2 loss
is used for low frequency output to better reconstruct aver-
age values. The supplementary material has architectural
diagrams for each block.
4. Experiments
We investigate the quality of representations learned from
Scale-MAE pretraining through a set of experiments that
explore their robustness to scale as well as their transfer
performance to additional tasks. First, we present our main
experiments in Section 4.1 and compare with SatMAE [13],
a current state-of-the-art MAE for remote sensing imagery,
Input Image Mask Low Frequency High Frequency ReconstructionFigure 4. Scale-MAE reconstruction. Examples from Functional
Map of the World are shown. From left to right, an input image
at 224x224 resolution is shown. Its corresponding mask is visual-
ized as well. Columns 3 and 4 show the low and high frequency
produced by the Scale-MAE decoder. The last column is the re-
construction obtained from summing the low and high frequency
features together.
ConvMAE [21], a state-of-the-art multiscale MAE, as well
as several other approaches detailed throughout. The exact
implementation of Scale-MAE for the main experiments was
determined through a set of ablation experiments presented
in Section 4.2.
We pretrain a ViT-Large model with Scale-MAE using the
Functional Map of the World (FMoW) [12] RGB training set,
which consists of 363.6k images of varying image resolution
and GSD, for 800 epochs. The initial higher resolution image
Ihris taken as a random 448px2crop of the input image, and
the input image Iis then a downsampled 224px2fromIhr.
The low frequency groundtruth is obtained by downscaling
Ihrto 14px2and then upscaling to 224px2, while the high
frequency groundtruth is obtained by downscaling Ihrto
56px2and then upscaling to 448px2and subtracting this
image from Ihr.
Figure 4 shows examples of the masked input, low resolu-
tion/frequency, high resolution/frequency, and combined re-
construction of FMoW images during training. The low res-
olution/frequency images capture color gradients and land-
scapes, while the residual high resolution/frequency images
capture object edges, roads, and building outlines.
4.1. Representation Quality
We evaluate the quality of representations from
Scale-MAE by freezing the encoder and performing a non-
parametric k-nearest-neighbor (kNN) classification with
eight different remote sensing imagery classification datasets
0 25% 50% 75% 100%0.50.60.70.80.91.0KNN acc.
RESISC
Scale-MAE
SatMAE
ConvMAE
0 25% 50% 75% 100%0.50.60.70.80.91.0
Optimal-31
0 25% 50% 75% 100%0.50.60.70.80.91.0
MLRSNet
0 25% 50% 75% 100%0.50.60.70.80.91.0
CV-BrCT
0 25% 50% 75% 100%
Relative GSD0.50.60.70.80.91.0KNN acc.
WHU-RS19
0 25% 50% 75% 100%
Relative GSD0.50.60.70.80.91.0
EuroSAT
0 25% 50% 75% 100%
Relative GSD0.50.60.70.80.91.0
AiRound
0 25% 50% 75% 100%
Relative GSD0.50.60.70.80.91.0
UC MercedFigure 5. Learning better representations at all scales. Scale-MAE (blue) features perform better than state-of-the-art. We evaluate kNN
accuracy on eight datasets with a large variance in GSD. Scale-MAE consistently produces better results at coarser resolutions. In addition
to using evaluation datasets at different GSDs, to further test the multiscale representations, we create multiple test sets for each dataset
in which we downsampled the full resolution validation set to coarser GSDs at fixed percentages: XG%
val, G∈ {12.5,25,50,100}, where
EuroSat does not include the 12.5% because the images are at a resolution of 64px, our patch size is 16px, and an input image of 8px is too
small.
with different GSDs, none of which were encountered dur-
ing pretraining. The kNN classifier operates by encoding
all train and validation instances, where each embedded in-
stance in the validation set computes the cosine distance

is 2x2 with stride 2 that outputs a feature map at 2x the in-

put resolution (28 in Figure 2), followed by a LayerNorm

and GELU, and then another 2x2 deconvolution layer that

outputs a feature maps at 2x the previous resolution (56 in

Figure 2). See the supplementary material for a full architec-

tural diagram.

Reconstruction After having been upsampled, the lower

resolution and higher resolution feature maps are passed into

Laplacian Blocks (LBs in Figure 2) that reconstruct high

and low resolution images for the high and low frequency

reconstruction, respectively. Architecturally, the Laplacian

Blocks consist of a sequence of three sub-blocks: a Lapla-

cian Feature Mapping Block, a Laplacian Upsample Block,

and a Laplacian Pyramid Reconstruction Block. The Feature

Mapping Block is used to project features within a particular

layer of the Laplacian Pyramid back to the RGB space. The

Laplacian Upsample Block represents a learnable upsam-

ple function that maps latent features from one layer of the

Laplacian Pyramid to a higher level. Finally, the Laplacian

Pyramid Reconstruction Block is used to reconstruct infor-

mation at the different frequencies in RGB space. Following

super resolution literature [2], an L1 loss is used for high

frequency output to better reconstruct edges and an L2 loss

is used for low frequency output to better reconstruct aver-

age values. The supplementary material has architectural

diagrams for each block.

4. Experiments

We investigate the quality of representations learned from

Scale-MAE pretraining through a set of experiments that

explore their robustness to scale as well as their transfer

performance to additional tasks. First, we present our main

experiments in Section 4.1 and compare with SatMAE [13],

a current state-of-the-art MAE for remote sensing imagery,

Input Image Mask Low Frequency High Frequency ReconstructionFigure 4. Scale-MAE reconstruction. Examples from Functional

Map of the World are shown. From left to right, an input image

at 224x224 resolution is shown. Its corresponding mask is visual-

ized as well. Columns 3 and 4 show the low and high frequency

produced by the Scale-MAE decoder. The last column is the re-

construction obtained from summing the low and high frequency

features together.

ConvMAE [21], a state-of-the-art multiscale MAE, as well

as several other approaches detailed throughout. The exact

implementation of Scale-MAE for the main experiments was

determined through a set of ablation experiments presented

in Section 4.2.

We pretrain a ViT-Large model with Scale-MAE using the

Functional Map of the World (FMoW) [12] RGB training set,

which consists of 363.6k images of varying image resolution

and GSD, for 800 epochs. The initial higher resolution image

Ihris taken as a random 448px2crop of the input image, and

the input image Iis then a downsampled 224px2fromIhr.

The low frequency groundtruth is obtained by downscaling

Ihrto 14px2and then upscaling to 224px2, while the high

frequency groundtruth is obtained by downscaling Ihrto

56px2and then upscaling to 448px2and subtracting this

image from Ihr.

Figure 4 shows examples of the masked input, low resolu-

tion/frequency, high resolution/frequency, and combined re-

construction of FMoW images during training. The low res-

olution/frequency images capture color gradients and land-

scapes, while the residual high resolution/frequency images

capture object edges, roads, and building outlines.

4.1. Representation Quality

We evaluate the quality of representations from

Scale-MAE by freezing the encoder and performing a non-

parametric k-nearest-neighbor (kNN) classification with

eight different remote sensing imagery classification datasets

0 25% 50% 75% 100%0.50.60.70.80.91.0KNN acc.

RESISC

Scale-MAE

SatMAE

ConvMAE

0 25% 50% 75% 100%0.50.60.70.80.91.0

Optimal-31

0 25% 50% 75% 100%0.50.60.70.80.91.0

MLRSNet

0 25% 50% 75% 100%0.50.60.70.80.91.0

CV-BrCT

0 25% 50% 75% 100%

Relative GSD0.50.60.70.80.91.0KNN acc.

WHU-RS19

0 25% 50% 75% 100%

Relative GSD0.50.60.70.80.91.0

EuroSAT

0 25% 50% 75% 100%

Relative GSD0.50.60.70.80.91.0

AiRound

0 25% 50% 75% 100%

Relative GSD0.50.60.70.80.91.0

UC MercedFigure 5. Learning better representations at all scales. Scale-MAE (blue) features perform better than state-of-the-art. We evaluate kNN

accuracy on eight datasets with a large variance in GSD. Scale-MAE consistently produces better results at coarser resolutions. In addition

to using evaluation datasets at different GSDs, to further test the multiscale representations, we create multiple test sets for each dataset

in which we downsampled the full resolution validation set to coarser GSDs at fixed percentages: XG%

val, G∈ {12.5,25,50,100}, where

EuroSat does not include the 12.5% because the images are at a resolution of 64px, our patch size is 16px, and an input image of 8px is too

small.

with different GSDs, none of which were encountered dur-

ing pretraining. The kNN classifier operates by encoding

all train and validation instances, where each embedded in-

stance in the validation set computes the cosine distance